Food Production Chain Identification and Traceability Systems: an analysis considering the perspective of a safe beef offer
Bibliographic record
Abstract
Beef is an important segment in the Brazilian agribusiness, with high share in the country exports value. This article aims at a discussion about the consistency of the Brazilian legislation that supports the Cattle and Buffalo Identification and Certification System (SISBOV), compared to the legislation of some pioneering countries that have been using identification and traceability systems in food production chains. The analysis, based upon Institutional Economics, involves an approach of the structure of the domestic cattle production, using secondary data made available by MAPA, SECEX/MDIC, IBGE, FAO and ABIEC, besides documents that establish the food safety policy, from MS and MAPA. The international legislation study was carried out from official documents from the United States of America, Canada and the European Union (EU). SISBOV was developed to comply with the exigencies imposed by the EU to import Brazilian beef. The adherence to that system involves several adjustments in the management of the elements of the beef production chain to make it feasible to export to the EU. From the point of view of its structure, that system complies with the exigencies of the European market, even though it seems to be more feasible to wealthier producers and slaughters. Further to that, SISBOV is, potentially, an inhibiting mechanism to occasional illegal practices such as clandestine slaughters and tax evading.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".